Orchestrate end‑to‑end data journeys within the ecosystem of unstructured financial data to generate actionable reporting insights.
Analyze existing processes across client mandates and translate them into detailed process flowcharts.
Understand business requirements and develop solutions aligned with industry best practices.
Design data pipelines and engineering infrastructure to support scalable machine learning systems.
Identify, assess, and integrate new technologies to enhance the performance, maintainability, and reliability of ML systems.
Facilitate the development and deployment of proof‑of‑concept AI solutions.
Collaborate with Product Managers, software engineers, and cross‑functional stakeholders to ensure effective deployment and operationalization of ML models.
Stay current with industry trends and advancements in MLOps.
Ensure all AI applications comply with data privacy, security, and ethical standards.
Serve as a subject‑matter expert on large language models and natural language processing for large‑scale unstructured data.
Design and optimize agentic AI systems that dynamically adapt to platform needs and enhance end‑user interactions.
Identify high‑impact AI use cases and integrate both off‑the‑shelf and custom‑built solutions to address business needs.
Requirements
Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
Strong foundation in AI concepts, including LLMs, RAG architectures, embedding, vector databases, prompt engineering, agent workflows, and guardrail design.
Hands‑on prototyping capability: building POCs, calling APIs, using basic Python/JavaScript for quick validation, parsing JSON, and running feasibility checks.
Expertise in workflow and agent design, including intent mapping, tool/actions definition, escalation paths, and multi‑step orchestration.
Working knowledge of system integration fundamentals: reading API documentation, understanding events and webhooks, managing data flows, and handling errors.
Ability to define evaluation frameworks and metrics: KPIs, prompt testing, A/B experimentation, performance measurement, and cost analysis.
Familiarity with security and reliability practices including PII handling, access control, logging, SLAs, and system monitoring.
Strong documentation and communication skills: writing clear requirements, process workflows, integration guides, and delivering concise updates.
Product‑driven mindset: mapping business processes, gathering user needs, defining MVPs, and prioritizing roadmaps.
Ability to collaborate effectively with engineering, AI/ML teams, QA, operations, and business stakeholders.
Knowledge of governance and continuous improvement practices: version control, change management, reusable templates, and optimization cycles.
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